Overview

Dataset statistics

Number of variables13
Number of observations7273
Missing cells5667
Missing cells (%)6.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory738.8 KiB
Average record size in memory104.0 B

Variable types

Categorical2
Numeric11

Alerts

Symbol has a high cardinality: 2240 distinct values High cardinality
ESG Score is highly correlated with Environmental Pillar Score and 2 other fieldsHigh correlation
Environmental Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Social Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Governance Pillar Score is highly correlated with ESG ScoreHigh correlation
semi-variance (down) is highly correlated with VaR (95%)High correlation
VaR (95%) is highly correlated with semi-variance (down)High correlation
ESG Score is highly correlated with Environmental Pillar Score and 2 other fieldsHigh correlation
Environmental Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Social Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Governance Pillar Score is highly correlated with ESG ScoreHigh correlation
semi-variance (down) is highly correlated with VaR (95%)High correlation
VaR (95%) is highly correlated with semi-variance (down)High correlation
ESG Score is highly correlated with Environmental Pillar Score and 1 other fieldsHigh correlation
Environmental Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Social Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
semi-variance (down) is highly correlated with VaR (95%)High correlation
VaR (95%) is highly correlated with semi-variance (down)High correlation
ESG Score is highly correlated with Environmental Pillar Score and 2 other fieldsHigh correlation
Environmental Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Social Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Governance Pillar Score is highly correlated with ESG ScoreHigh correlation
mean-return is highly correlated with VaR (95%)High correlation
semi-variance (down) is highly correlated with VaR (95%)High correlation
kurtosis is highly correlated with skewHigh correlation
skew is highly correlated with kurtosisHigh correlation
VaR (95%) is highly correlated with mean-return and 1 other fieldsHigh correlation
D(ESG, kurtosis) has 4336 (59.6%) missing values Missing
D(ESG, VaR) has 1331 (18.3%) missing values Missing
semi-variance (down) is highly skewed (γ1 = 43.67768601) Skewed
Symbol is uniformly distributed Uniform
ESG Score has unique values Unique
semi-variance (down) has unique values Unique
kurtosis has unique values Unique
skew has unique values Unique

Reproduction

Analysis started2022-09-29 03:27:04.743651
Analysis finished2022-09-29 03:27:17.590226
Duration12.85 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Symbol
Categorical

HIGH CARDINALITY
UNIFORM

Distinct2240
Distinct (%)30.8%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
KINS.OQ
 
5
HWKN.OQ
 
5
HOFT.OQ
 
5
HOLX.OQ
 
5
HOUS.N
 
5
Other values (2235)
7248 

Length

Max length8
Median length7
Mean length5.834593703
Min length3

Characters and Unicode

Total characters42435
Distinct characters35
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique409 ?
Unique (%)5.6%

Sample

1st row360.AX
2nd row360.AX
3rd rowA.N
4th rowA.N
5th rowA.N

Common Values

ValueCountFrequency (%)
KINS.OQ5
 
0.1%
HWKN.OQ5
 
0.1%
HOFT.OQ5
 
0.1%
HOLX.OQ5
 
0.1%
HOUS.N5
 
0.1%
HP.N5
 
0.1%
HPP.N5
 
0.1%
HPQ.N5
 
0.1%
HRI.N5
 
0.1%
HRTG.N5
 
0.1%
Other values (2230)7223
99.3%

Length

2022-09-29T04:27:17.635236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kins.oq5
 
0.1%
mei.n5
 
0.1%
met.n5
 
0.1%
meta.oq5
 
0.1%
mgm.n5
 
0.1%
mgpi.oq5
 
0.1%
mhh.a5
 
0.1%
mho.n5
 
0.1%
mkc.n5
 
0.1%
mlab.oq5
 
0.1%
Other values (2230)7223
99.3%

Most occurring characters

ValueCountFrequency (%)
.7273
17.1%
N5470
12.9%
O3982
 
9.4%
Q3090
 
7.3%
C1907
 
4.5%
A1814
 
4.3%
S1654
 
3.9%
T1583
 
3.7%
R1569
 
3.7%
M1247
 
2.9%
Other values (25)12846
30.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter35120
82.8%
Other Punctuation7273
 
17.1%
Lowercase Letter33
 
0.1%
Decimal Number6
 
< 0.1%
Connector Punctuation3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N5470
15.6%
O3982
 
11.3%
Q3090
 
8.8%
C1907
 
5.4%
A1814
 
5.2%
S1654
 
4.7%
T1583
 
4.5%
R1569
 
4.5%
M1247
 
3.6%
I1244
 
3.5%
Other values (16)11560
32.9%
Lowercase Letter
ValueCountFrequency (%)
a20
60.6%
b9
27.3%
p3
 
9.1%
q1
 
3.0%
Decimal Number
ValueCountFrequency (%)
32
33.3%
62
33.3%
02
33.3%
Other Punctuation
ValueCountFrequency (%)
.7273
100.0%
Connector Punctuation
ValueCountFrequency (%)
_3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin35153
82.8%
Common7282
 
17.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
N5470
15.6%
O3982
 
11.3%
Q3090
 
8.8%
C1907
 
5.4%
A1814
 
5.2%
S1654
 
4.7%
T1583
 
4.5%
R1569
 
4.5%
M1247
 
3.5%
I1244
 
3.5%
Other values (20)11593
33.0%
Common
ValueCountFrequency (%)
.7273
99.9%
_3
 
< 0.1%
32
 
< 0.1%
62
 
< 0.1%
02
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII42435
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.7273
17.1%
N5470
12.9%
O3982
 
9.4%
Q3090
 
7.3%
C1907
 
4.5%
A1814
 
4.3%
S1654
 
3.9%
T1583
 
3.7%
R1569
 
3.7%
M1247
 
2.9%
Other values (25)12846
30.3%

Year
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
2020.0
1858 
2019.0
1570 
2021.0
1558 
2018.0
1244 
2017.0
1043 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters43638
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020.0
2nd row2021.0
3rd row2017.0
4th row2018.0
5th row2019.0

Common Values

ValueCountFrequency (%)
2020.01858
25.5%
2019.01570
21.6%
2021.01558
21.4%
2018.01244
17.1%
2017.01043
14.3%

Length

2022-09-29T04:27:17.703251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-29T04:27:17.786269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2020.01858
25.5%
2019.01570
21.6%
2021.01558
21.4%
2018.01244
17.1%
2017.01043
14.3%

Most occurring characters

ValueCountFrequency (%)
016404
37.6%
210689
24.5%
.7273
16.7%
15415
 
12.4%
91570
 
3.6%
81244
 
2.9%
71043
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36365
83.3%
Other Punctuation7273
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
016404
45.1%
210689
29.4%
15415
 
14.9%
91570
 
4.3%
81244
 
3.4%
71043
 
2.9%
Other Punctuation
ValueCountFrequency (%)
.7273
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common43638
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
016404
37.6%
210689
24.5%
.7273
16.7%
15415
 
12.4%
91570
 
3.6%
81244
 
2.9%
71043
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII43638
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
016404
37.6%
210689
24.5%
.7273
16.7%
15415
 
12.4%
91570
 
3.6%
81244
 
2.9%
71043
 
2.4%

ESG Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct7273
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.5951191
Minimum5.114833664
Maximum94.44445553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.9 KiB
2022-09-29T04:27:17.874289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5.114833664
5-th percentile16.99017483
Q131.08531755
median43.89661443
Q359.68062035
95-th percentile78.64482082
Maximum94.44445553
Range89.32962187
Interquartile range (IQR)28.5953028

Descriptive statistics

Standard deviation18.86600105
Coefficient of variation (CV)0.4137723823
Kurtosis-0.728016009
Mean45.5951191
Median Absolute Deviation (MAD)14.05217136
Skewness0.2459283091
Sum331613.3012
Variance355.9259955
MonotonicityNot monotonic
2022-09-29T04:27:17.956307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.081018031
 
< 0.1%
39.770670731
 
< 0.1%
26.861619181
 
< 0.1%
28.016311351
 
< 0.1%
32.788465091
 
< 0.1%
30.888134991
 
< 0.1%
69.344181231
 
< 0.1%
61.585981241
 
< 0.1%
68.482824611
 
< 0.1%
49.978414561
 
< 0.1%
Other values (7263)7263
99.9%
ValueCountFrequency (%)
5.1148336641
< 0.1%
5.7764849911
< 0.1%
5.9749974951
< 0.1%
6.1522211931
< 0.1%
6.2216585671
< 0.1%
6.6863983551
< 0.1%
6.814182151
< 0.1%
6.8681014031
< 0.1%
6.9663795891
< 0.1%
7.2218059131
< 0.1%
ValueCountFrequency (%)
94.444455531
< 0.1%
93.539125651
< 0.1%
93.327841241
< 0.1%
92.806508361
< 0.1%
92.619923141
< 0.1%
92.315162631
< 0.1%
92.14299521
< 0.1%
91.905084061
< 0.1%
91.855033491
< 0.1%
91.476861231
< 0.1%

Environmental Pillar Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6362
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.68848524
Minimum0.027777778
Maximum97.98022505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.9 KiB
2022-09-29T04:27:18.039326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.027777778
5-th percentile1.771771772
Q18.796296296
median25.95088648
Q353.125
95-th percentile81.67524908
Maximum97.98022505
Range97.95244727
Interquartile range (IQR)44.3287037

Descriptive statistics

Standard deviation26.34713048
Coefficient of variation (CV)0.8060064664
Kurtosis-0.8601953105
Mean32.68848524
Median Absolute Deviation (MAD)19.68115939
Skewness0.5904826047
Sum237743.3532
Variance694.1712847
MonotonicityNot monotonic
2022-09-29T04:27:18.122344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.74058178452
 
0.7%
1.77177177239
 
0.5%
1.60882140333
 
0.5%
4.94505494518
 
0.2%
37.3170731718
 
0.2%
1.92481884116
 
0.2%
6.30252100816
 
0.2%
3.46097201814
 
0.2%
3.23694432514
 
0.2%
313
 
0.2%
Other values (6352)7040
96.8%
ValueCountFrequency (%)
0.0277777781
< 0.1%
0.0869565221
< 0.1%
0.0885935771
< 0.1%
0.1424501421
< 0.1%
0.1462971261
< 0.1%
0.1944444441
< 0.1%
0.2170138891
< 0.1%
0.2555583951
< 0.1%
0.2946127951
< 0.1%
0.3086419751
< 0.1%
ValueCountFrequency (%)
97.980225051
< 0.1%
97.697050951
< 0.1%
97.168150591
< 0.1%
97.160908831
< 0.1%
97.022995931
< 0.1%
96.679221371
< 0.1%
96.622060021
< 0.1%
96.542736541
< 0.1%
96.33446141
< 0.1%
96.075148811
< 0.1%

Social Pillar Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7242
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.40329011
Minimum0.453490413
Maximum97.83313378
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.9 KiB
2022-09-29T04:27:18.210364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.453490413
5-th percentile15.33759499
Q130.70256215
median45.91989914
Q363.01730829
95-th percentile84.72395018
Maximum97.83313378
Range97.37964337
Interquartile range (IQR)32.31474614

Descriptive statistics

Standard deviation21.21297296
Coefficient of variation (CV)0.4475000134
Kurtosis-0.7505785007
Mean47.40329011
Median Absolute Deviation (MAD)16.05144928
Skewness0.2364710717
Sum344764.129
Variance449.9902217
MonotonicityNot monotonic
2022-09-29T04:27:18.297383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.953331054
 
0.1%
28.965558253
 
< 0.1%
503
 
< 0.1%
23.843056713
 
< 0.1%
22.472511383
 
< 0.1%
31.068697953
 
< 0.1%
25.10542163
 
< 0.1%
25.916863393
 
< 0.1%
37.52
 
< 0.1%
29.705935862
 
< 0.1%
Other values (7232)7244
99.6%
ValueCountFrequency (%)
0.4534904131
< 0.1%
1.2509850281
< 0.1%
1.3741856681
< 0.1%
2.0584982181
< 0.1%
2.2603485841
< 0.1%
2.4409562211
< 0.1%
2.7040716291
< 0.1%
2.7087430721
< 0.1%
2.8623239941
< 0.1%
2.8683574881
< 0.1%
ValueCountFrequency (%)
97.833133781
< 0.1%
97.668742951
< 0.1%
97.655062681
< 0.1%
97.579012441
< 0.1%
97.394131821
< 0.1%
97.392935251
< 0.1%
97.348331381
< 0.1%
97.256723051
< 0.1%
97.234743751
< 0.1%
97.156834661
< 0.1%

Governance Pillar Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7246
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.30286473
Minimum0.713528414
Maximum99.49668624
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.9 KiB
2022-09-29T04:27:18.385985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.713528414
5-th percentile16.39364647
Q136.92061081
median54.97081144
Q370.36096517
95-th percentile85.49530137
Maximum99.49668624
Range98.78315783
Interquartile range (IQR)33.44035436

Descriptive statistics

Standard deviation21.48435869
Coefficient of variation (CV)0.4030619892
Kurtosis-0.8086517305
Mean53.30286473
Median Absolute Deviation (MAD)16.55039119
Skewness-0.2177612235
Sum387671.7352
Variance461.5776683
MonotonicityNot monotonic
2022-09-29T04:27:18.473006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70.2230462
 
< 0.1%
51.315585142
 
< 0.1%
60.886359212
 
< 0.1%
25.723148152
 
< 0.1%
24.663358632
 
< 0.1%
49.206947782
 
< 0.1%
35.748197452
 
< 0.1%
62.462575712
 
< 0.1%
50.87129632
 
< 0.1%
57.423151642
 
< 0.1%
Other values (7236)7253
99.7%
ValueCountFrequency (%)
0.7135284141
< 0.1%
1.218940231
< 0.1%
1.4498058791
< 0.1%
1.6215289681
< 0.1%
1.7419423241
< 0.1%
1.773748941
< 0.1%
2.0732113141
< 0.1%
2.2232824431
< 0.1%
2.3250636131
< 0.1%
2.4625757061
< 0.1%
ValueCountFrequency (%)
99.496686241
< 0.1%
98.610655441
< 0.1%
98.14167411
< 0.1%
97.52325211
< 0.1%
97.499648791
< 0.1%
97.301438521
< 0.1%
97.028115551
< 0.1%
96.773164111
< 0.1%
96.673930581
< 0.1%
96.593458711
< 0.1%

mean-return
Real number (ℝ)

HIGH CORRELATION

Distinct7268
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001136985144
Minimum-0.552090105
Maximum0.5359062215
Zeros0
Zeros (%)0.0%
Negative3186
Negative (%)43.8%
Memory size56.9 KiB
2022-09-29T04:27:18.563622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.552090105
5-th percentile-0.07660494631
Q1-0.01780396201
median0.004320490956
Q30.0235602911
95-th percentile0.06587996399
Maximum0.5359062215
Range1.087996327
Interquartile range (IQR)0.04136425311

Descriptive statistics

Standard deviation0.04781232323
Coefficient of variation (CV)42.05184517
Kurtosis13.28160525
Mean0.001136985144
Median Absolute Deviation (MAD)0.02032985182
Skewness-0.9680795522
Sum8.269292952
Variance0.002286018253
MonotonicityNot monotonic
2022-09-29T04:27:18.650856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.063013380052
 
< 0.1%
0.031460944612
 
< 0.1%
0.011522882332
 
< 0.1%
1.513940488 × 10-172
 
< 0.1%
0.0394704742
 
< 0.1%
-0.069478421071
 
< 0.1%
-0.030500991861
 
< 0.1%
0.024159993411
 
< 0.1%
0.062357868441
 
< 0.1%
0.030563173331
 
< 0.1%
Other values (7258)7258
99.8%
ValueCountFrequency (%)
-0.5520901051
< 0.1%
-0.53041388231
< 0.1%
-0.49257455991
< 0.1%
-0.38965559381
< 0.1%
-0.33464189151
< 0.1%
-0.33079360641
< 0.1%
-0.313157331
< 0.1%
-0.29743802221
< 0.1%
-0.28891398461
< 0.1%
-0.28304684631
< 0.1%
ValueCountFrequency (%)
0.53590622151
< 0.1%
0.28352657881
< 0.1%
0.25580976621
< 0.1%
0.24944734791
< 0.1%
0.23766776791
< 0.1%
0.23212046331
< 0.1%
0.22828886541
< 0.1%
0.22062058531
< 0.1%
0.21998294721
< 0.1%
0.20967489721
< 0.1%

semi-variance (down)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
UNIQUE

Distinct7273
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03301529356
Minimum2.049697647 × 10-6
Maximum10.60379622
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.9 KiB
2022-09-29T04:27:18.742876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.049697647 × 10-6
5-th percentile0.001243005579
Q10.004228974024
median0.01011033186
Q30.02653212721
95-th percentile0.1172569894
Maximum10.60379622
Range10.60379417
Interquartile range (IQR)0.02230315318

Descriptive statistics

Standard deviation0.1617104052
Coefficient of variation (CV)4.898045353
Kurtosis2617.090121
Mean0.03301529356
Median Absolute Deviation (MAD)0.007417430372
Skewness43.67768601
Sum240.1202301
Variance0.02615025515
MonotonicityNot monotonic
2022-09-29T04:27:18.830895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0040211727321
 
< 0.1%
0.025829470161
 
< 0.1%
0.020493808861
 
< 0.1%
0.0044654215191
 
< 0.1%
0.014459467841
 
< 0.1%
0.010636442021
 
< 0.1%
0.005292361281
 
< 0.1%
0.021663096971
 
< 0.1%
0.022363950961
 
< 0.1%
0.011910459111
 
< 0.1%
Other values (7263)7263
99.9%
ValueCountFrequency (%)
2.049697647 × 10-61
< 0.1%
5.055098442 × 10-61
< 0.1%
5.08445488 × 10-61
< 0.1%
5.274323069 × 10-61
< 0.1%
6.131105551 × 10-61
< 0.1%
8.070466148 × 10-61
< 0.1%
1.132803218 × 10-51
< 0.1%
1.166024936 × 10-51
< 0.1%
1.237343283 × 10-51
< 0.1%
1.319121727 × 10-51
< 0.1%
ValueCountFrequency (%)
10.603796221
< 0.1%
4.4012740891
< 0.1%
2.8147659031
< 0.1%
2.6603798661
< 0.1%
2.5282397241
< 0.1%
1.6839319681
< 0.1%
1.2739383671
< 0.1%
1.1822638031
< 0.1%
1.1623104791
< 0.1%
1.1290216991
< 0.1%

kurtosis
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct7273
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7105823339
Minimum-4.771690305
Maximum10.66785002
Zeros0
Zeros (%)0.0%
Negative3172
Negative (%)43.6%
Memory size56.9 KiB
2022-09-29T04:27:18.920915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-4.771690305
5-th percentile-1.375287965
Q1-0.6194564829
median0.2235622109
Q31.512164106
95-th percentile4.510751604
Maximum10.66785002
Range15.43954032
Interquartile range (IQR)2.131620589

Descriptive statistics

Standard deviation1.865801242
Coefficient of variation (CV)2.625735475
Kurtosis2.221970281
Mean0.7105823339
Median Absolute Deviation (MAD)0.9802293374
Skewness1.38930909
Sum5168.065314
Variance3.481214276
MonotonicityNot monotonic
2022-09-29T04:27:19.009935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.63282574091
 
< 0.1%
4.428689741
 
< 0.1%
2.7831773921
 
< 0.1%
0.11099740281
 
< 0.1%
-0.80317829441
 
< 0.1%
0.12014619241
 
< 0.1%
-0.50982369261
 
< 0.1%
0.78728045911
 
< 0.1%
-0.60798256121
 
< 0.1%
1.2945568221
 
< 0.1%
Other values (7263)7263
99.9%
ValueCountFrequency (%)
-4.7716903051
< 0.1%
-3.3034801381
< 0.1%
-3.2836624931
< 0.1%
-3.0102317641
< 0.1%
-2.7722840671
< 0.1%
-2.7681182631
< 0.1%
-2.7533305481
< 0.1%
-2.7195872971
< 0.1%
-2.7124496211
< 0.1%
-2.6694165281
< 0.1%
ValueCountFrequency (%)
10.667850021
< 0.1%
10.396198261
< 0.1%
10.392304821
< 0.1%
9.9911235261
< 0.1%
9.3306737851
< 0.1%
9.2727962921
< 0.1%
9.1362195551
< 0.1%
8.9692546151
< 0.1%
8.9098639571
< 0.1%
8.8672274541
< 0.1%

skew
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct7273
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1679193383
Minimum-3.243905543
Maximum3.118139779
Zeros1
Zeros (%)< 0.1%
Negative4225
Negative (%)58.1%
Memory size56.9 KiB
2022-09-29T04:27:19.098955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-3.243905543
5-th percentile-1.661676494
Q1-0.7366655895
median-0.1743918841
Q30.4104441429
95-th percentile1.322835232
Maximum3.118139779
Range6.362045322
Interquartile range (IQR)1.147109732

Descriptive statistics

Standard deviation0.8954242476
Coefficient of variation (CV)-5.332466507
Kurtosis0.2061299936
Mean-0.1679193383
Median Absolute Deviation (MAD)0.5719643003
Skewness0.05438400105
Sum-1221.277348
Variance0.8017845832
MonotonicityNot monotonic
2022-09-29T04:27:19.528051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.061141406491
 
< 0.1%
-1.5976176591
 
< 0.1%
-0.97968088141
 
< 0.1%
-0.69684938321
 
< 0.1%
-0.36550786021
 
< 0.1%
-0.64399726321
 
< 0.1%
0.92767414521
 
< 0.1%
-0.7288489081
 
< 0.1%
-0.15346238551
 
< 0.1%
0.69526624071
 
< 0.1%
Other values (7263)7263
99.9%
ValueCountFrequency (%)
-3.2439055431
< 0.1%
-3.1926246111
< 0.1%
-3.1921451011
< 0.1%
-2.957153091
< 0.1%
-2.915910351
< 0.1%
-2.84101981
< 0.1%
-2.8164677981
< 0.1%
-2.8032260221
< 0.1%
-2.7941864191
< 0.1%
-2.7928008081
< 0.1%
ValueCountFrequency (%)
3.1181397791
< 0.1%
2.9395501841
< 0.1%
2.9006242261
< 0.1%
2.8469504781
< 0.1%
2.8150819581
< 0.1%
2.7816161991
< 0.1%
2.7276243631
< 0.1%
2.6740595041
< 0.1%
2.5790034581
< 0.1%
2.5752330831
< 0.1%

VaR (95%)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7271
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1812995785
Minimum-5.756462732
Maximum0.05449763172
Zeros1
Zeros (%)< 0.1%
Negative7254
Negative (%)99.7%
Memory size56.9 KiB
2022-09-29T04:27:19.617071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-5.756462732
5-th percentile-0.4711901745
Q1-0.2285139713
median-0.1372989011
Q3-0.07893893959
95-th percentile-0.03226260506
Maximum0.05449763172
Range5.810960364
Interquartile range (IQR)0.1495750318

Descriptive statistics

Standard deviation0.174921993
Coefficient of variation (CV)-0.9648229435
Kurtosis166.4759273
Mean-0.1812995785
Median Absolute Deviation (MAD)0.06885300129
Skewness-7.360293362
Sum-1318.591834
Variance0.03059770363
MonotonicityNot monotonic
2022-09-29T04:27:19.702090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.26531412552
 
< 0.1%
-0.34657359032
 
< 0.1%
-0.071075468581
 
< 0.1%
-0.17676492921
 
< 0.1%
-0.22316261231
 
< 0.1%
-0.14515778621
 
< 0.1%
-0.036031849961
 
< 0.1%
-0.1841441441
 
< 0.1%
-0.14834655671
 
< 0.1%
-0.13389848161
 
< 0.1%
Other values (7261)7261
99.8%
ValueCountFrequency (%)
-5.7564627321
< 0.1%
-3.4538776391
< 0.1%
-1.6699980661
< 0.1%
-1.644567561
< 0.1%
-1.6279021631
< 0.1%
-1.6094379121
< 0.1%
-1.5652834211
< 0.1%
-1.5306459461
< 0.1%
-1.4664408041
< 0.1%
-1.4402485051
< 0.1%
ValueCountFrequency (%)
0.054497631721
< 0.1%
0.051156618591
< 0.1%
0.032876869491
< 0.1%
0.027034805881
< 0.1%
0.013517765121
< 0.1%
0.010063965991
< 0.1%
0.0057931581391
< 0.1%
0.0049907767871
< 0.1%
0.0042718969861
< 0.1%
0.0039456738281
< 0.1%

D(ESG, kurtosis)
Real number (ℝ≥0)

MISSING

Distinct2937
Distinct (%)100.0%
Missing4336
Missing (%)59.6%
Infinite0
Infinite (%)0.0%
Mean2.709579019
Minimum6.79050927 × 10-8
Maximum50.90867194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.9 KiB
2022-09-29T04:27:19.787334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6.79050927 × 10-8
5-th percentile0.006824963715
Q10.1760470753
median0.8848366336
Q33.066449194
95-th percentile12.4814052
Maximum50.90867194
Range50.90867188
Interquartile range (IQR)2.890402118

Descriptive statistics

Standard deviation4.706685144
Coefficient of variation (CV)1.737054026
Kurtosis18.45084039
Mean2.709579019
Median Absolute Deviation (MAD)0.8400479742
Skewness3.586007949
Sum7958.033579
Variance22.15288504
MonotonicityNot monotonic
2022-09-29T04:27:19.877582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.0785636371
 
< 0.1%
5.808389851
 
< 0.1%
0.35222659291
 
< 0.1%
0.79816458581
 
< 0.1%
1.8810866051
 
< 0.1%
4.7249633441
 
< 0.1%
0.67896944131
 
< 0.1%
0.32027011441
 
< 0.1%
28.381550651
 
< 0.1%
0.52185270931
 
< 0.1%
Other values (2927)2927
40.2%
(Missing)4336
59.6%
ValueCountFrequency (%)
6.79050927 × 10-81
< 0.1%
2.920950538 × 10-61
< 0.1%
4.884088178 × 10-61
< 0.1%
4.986095251 × 10-61
< 0.1%
6.958513263 × 10-61
< 0.1%
1.848653528 × 10-51
< 0.1%
1.863846349 × 10-51
< 0.1%
2.626211387 × 10-51
< 0.1%
2.637262071 × 10-51
< 0.1%
2.793033647 × 10-51
< 0.1%
ValueCountFrequency (%)
50.908671941
< 0.1%
45.295430761
< 0.1%
44.084114241
< 0.1%
40.111824341
< 0.1%
39.947886331
< 0.1%
38.528657721
< 0.1%
33.780347111
< 0.1%
33.496362731
< 0.1%
32.239119631
< 0.1%
31.066719181
< 0.1%

D(ESG, VaR)
Real number (ℝ≥0)

MISSING

Distinct5942
Distinct (%)100.0%
Missing1331
Missing (%)18.3%
Infinite0
Infinite (%)0.0%
Mean1.033419998
Minimum7.201370445 × 10-8
Maximum47.28022229
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.9 KiB
2022-09-29T04:27:19.965603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum7.201370445 × 10-8
5-th percentile0.003865694903
Q10.09087302745
median0.3933587679
Q31.166821518
95-th percentile3.91738911
Maximum47.28022229
Range47.28022221
Interquartile range (IQR)1.07594849

Descriptive statistics

Standard deviation2.110622726
Coefficient of variation (CV)2.04236683
Kurtosis125.297544
Mean1.033419998
Median Absolute Deviation (MAD)0.3625595722
Skewness8.423620273
Sum6140.581629
Variance4.454728292
MonotonicityNot monotonic
2022-09-29T04:27:20.052623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3054712681
 
< 0.1%
0.72369877081
 
< 0.1%
1.8122751961
 
< 0.1%
0.73368664531
 
< 0.1%
1.2191049891
 
< 0.1%
0.20877703951
 
< 0.1%
2.4842051421
 
< 0.1%
0.8488468791
 
< 0.1%
0.0071358064471
 
< 0.1%
0.11703655311
 
< 0.1%
Other values (5932)5932
81.6%
(Missing)1331
 
18.3%
ValueCountFrequency (%)
7.201370445 × 10-81
< 0.1%
1.615828905 × 10-71
< 0.1%
3.591858258 × 10-71
< 0.1%
5.596244496 × 10-71
< 0.1%
7.547698111 × 10-71
< 0.1%
9.188274723 × 10-71
< 0.1%
1.053538485 × 10-61
< 0.1%
1.207528765 × 10-61
< 0.1%
2.423971339 × 10-61
< 0.1%
2.98789117 × 10-61
< 0.1%
ValueCountFrequency (%)
47.280222291
< 0.1%
47.011040471
< 0.1%
37.174199171
< 0.1%
35.044973961
< 0.1%
28.141407191
< 0.1%
25.853308391
< 0.1%
22.905634731
< 0.1%
22.430017651
< 0.1%
20.238895161
< 0.1%
19.800864641
< 0.1%

Interactions

2022-09-29T04:27:16.384443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:07.830920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:08.615589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:09.428067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:10.447952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:11.248130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:12.061313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:12.868493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:13.695678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:14.768751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:15.584265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:16.453458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:07.904937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:08.687605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:09.495082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:10.516968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:11.317146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:12.130329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:12.938508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:13.765693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:14.840765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:15.650279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:16.523474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:07.974952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:08.762622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:09.565098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:10.587983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:11.393162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:12.204345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:13.017526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:13.836709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:14.917782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:15.721295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:16.592489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:08.045462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:08.835638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:09.635114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:10.661000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:11.467180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:12.278362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:13.093544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:13.909725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:14.990799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:15.794311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:16.666505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:08.114478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:08.910655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:09.708130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:10.733015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:11.539195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:12.351378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:13.169560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:13.980741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:15.066817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:15.866327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:16.730520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:08.187494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:08.984676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:09.782147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:10.807032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:11.612211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:12.425395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:13.245577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:14.053590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:15.142165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:15.944345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:16.800535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:08.257509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:09.058985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:10.073211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:10.880048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:11.689229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:12.498410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:13.323594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:14.126606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:15.213181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:16.018362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:16.879553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:08.331526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:09.136002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:10.149228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:10.954065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:11.769246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:12.575428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:13.402612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:14.201623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:15.292199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:16.092377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:16.951569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:08.405542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:09.210018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:10.228246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:11.029082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:11.844263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:12.649444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:13.480629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:14.272639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:15.370217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:16.163393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:17.027586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:08.477558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:09.285036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:10.304262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:11.105098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:11.919280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:12.723461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:13.554646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:14.620717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:15.441232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:16.241410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:17.096601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:08.546574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:09.359051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:10.377279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:11.177114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:11.994298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:12.795477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:13.625662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:14.694733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:15.514248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-29T04:27:16.315427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-09-29T04:27:20.129640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-29T04:27:20.247666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-29T04:27:20.363015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-29T04:27:20.480188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-29T04:27:17.208627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-29T04:27:17.364297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-29T04:27:17.467199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-29T04:27:17.517210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

SymbolYearESG ScoreEnvironmental Pillar ScoreSocial Pillar ScoreGovernance Pillar Scoremean-returnsemi-variance (down)kurtosisskewVaR (95%)D(ESG, kurtosis)D(ESG, VaR)
0360.AX2020.027.0810188.91666743.01514418.8092710.0328460.004021-0.6328260.061141-0.071075NaNNaN
1360.AX2021.031.4109588.45360845.97221025.7593070.0258250.0058391.742724-0.646043-0.075298NaN0.008210
2A.N2017.087.59506577.06065892.79237585.6679960.0355120.0070120.4557900.720648-0.096525NaNNaN
3A.N2018.089.48925378.04573794.20181788.624684-0.0610430.0044510.6103080.850044-0.1626270.0731910.250266
4A.N2019.088.33085579.33599794.50527384.398806-0.0292690.0160820.126243-0.828252-0.2284902.4421250.124653
5A.N2020.087.57748979.95897793.59937083.2039840.0456530.2709804.900414-1.988309-0.52022713.4500790.691122
6AA.N2017.087.27310990.33374879.47214995.671099-0.0217860.0023200.566293-0.682118-0.0811017.5587670.060957
7AA.N2018.086.61997888.38353779.12899596.369097-0.0337960.006287-0.703649-0.791101-0.137931NaN0.290054
8AA.N2019.088.07882587.34716383.47424796.673931-0.0396990.0151112.266191-1.274915-0.223285NaN0.216216
9AA.N2020.087.75377186.77328983.21756996.593459-0.0370210.2157375.702735-2.142136-0.5571650.8584820.842930

Last rows

SymbolYearESG ScoreEnvironmental Pillar ScoreSocial Pillar ScoreGovernance Pillar Scoremean-returnsemi-variance (down)kurtosisskewVaR (95%)D(ESG, kurtosis)D(ESG, VaR)
7263ZUMZ.OQ2019.026.0315595.59440630.46775131.321035-0.0374720.0497563.683881-1.761135-0.314004NaN0.903151
7264ZUMZ.OQ2020.025.9396604.88372130.83408231.029383-0.0052000.0673490.572611-0.974624-0.3854423.4520850.043482
7265ZUMZ.OQ2021.024.2570564.94949527.18347530.6592590.0214740.0037973.0690991.620179-0.0786033.0485102.319293
7266ZUO.N2020.036.2875293.08333354.97065830.1605370.0172910.0185351.256732-0.901465-0.182401NaNNaN
7267ZUO.N2021.053.91878527.84870166.40737750.9857440.0628550.002580-0.5513960.410789-0.016845NaN7.718136
7268ZWS.N2017.021.7566034.25006027.08554135.777625-0.0290820.1070022.814027-1.311767-0.491496NaN0.894813
7269ZWS.N2018.022.9664534.19339824.53159843.442041-0.1276200.074135-1.053860-0.274952-0.461663NaN0.013628
7270ZWS.N2019.042.38505947.92359436.35408243.6460980.0129170.0354426.0092752.079474-0.296457NaN1.114489
7271ZWS.N2020.059.56629853.79715376.73664343.977042-0.0384990.057422-0.321493-0.129591-0.393005NaN0.003407
7272ZWS.N2021.059.80939170.17463676.56941925.396839-0.0174650.0105180.3711961.074856-0.182711NaN0.592885